Dr. K Raghu is currently working as an Associate Professor in the Department of Computer Science and Engineering at Geethanjali College of Engineering and Technology, Hyderabad, India. He completed his PhD from Kakatiya University, Warangal. His research interests include speech processing, computer vision, pattern recognition, and Data Mining. He has authored 10 research papers, with 6 indexed in Scopus, 2 in SCI and others in reputed journals. He acted as a reviewer for various International conferences and can be contacted at email: raghukuphd@.
Design and Analysis of Embedded Sensors for IIoT: A Systematic Review Kogila Raghu, Macharla Mounika AI Driven Iot Systems for Industry 4 0, 2024 The Industrial Internet of Things (IoT) (IIoT) is the next generation of IoT technology, and it is exceptional in how its industrial domain application has entirely changed. Embedded sensors have become a vital part of many sectors over the past few years, revolutionizing how industrial processes are automated. Manufacturing procedures are simplified, performance is improved, and efficiency is optimized since these methods have been incorporated into tools and machinery. Market.us study report proposes that the size of the worldwide embedded systems industry will achieve US$173.4 billion by 2032. In this chapter, we presented a systematic review of different industrial sensors and their designs, as well as how they are applied in industry, including real-time applications, problems, and future directions.
SPEECH EMOTION RECOGNITION SYSTEM PERFORMANCE ANALYSIS WITH OPTIMIZED FEATURES USING DIFFERENT CLASSIFICATION ALGORITHMS Journal of Theoretical and Applied Information Technology, 2023
Emotion Recognition from Speech Utterances with Hybrid Spectral Features Using Machine Learning Algorithms Kogila Raghu, Manchala Sadanandam Traitement Du Signal, 2022 Speech Emotion Recognition is always a complicated task in the domain of Speech Processing Research, though many research works have been done. The first and foremost challenge of SER is to selecting the Speech Emotion Database (Corpora), then extracting the related speech features and finally construct an appropriate Classification model. An effort is created during this work to discover the speech prosodies, spectral and combination of features with their dynamism to illustrate and classify the emotions of speech signal. The intrinsic or fine variations of speech samples are combined with the static delivery parameters within the Speech Emotion Recognition (SER) to refine the accuracy. The work in this paper, carried out the experiments on RAVDESS, IIITH IIITH-TEMD and our developed Database of native language DETL (Database for Emotions in Telugu Language) Speech Emotion Databases. This work extracted features like MFCC and Hybrid Features (MFCC+ΔMFCC+ΔΔMFCC) then finally applied those individual features and Combination of Features to different Classification models like SVM and MLP. We have got approximately 75%, 78% and 81% of accuracy for MLP with hybrid combination features on the above Databases respectively.
A Perspective Study on Speech Emotion Recognition: Databases, Features and Classification Models Kogila Raghu, Manchala Sadanandam Traitement Du Signal, 2021 Automatic Speech Recognition (ASR) is a popular research area with many variations in human behaviour functionalities and interactions. Human beings want speech for communication and Conversations. When the conversation is going on, the information or message of the speech utterances is transferred. It also consists of message which includes speaker’s traits like emotion, his or her physiological characteristics and environmental statistics. There is a tremendous number of signals or records that are complex and encoded, but these can be decoded quickly because of human intelligence. Many academics in the domain of Human Computer Interaction (HCI) are working to automate speech generation and the extraction of speech attributes and meaning. For example, ASR can regulate the usage of voice command and maintain dictation discipline while also recognizing and verifying the speech of the speaker. As a result of accent and nativity traits, the speaker's emotional state can be discerned from the speech. In this Paper, we discussed Speech Production System of Human, Research Problems in Speech Processing, SER system Motivation, Challenges and Objectives of Speech Emotion Recognition, so far the work done on Telugu Speech Emotion Databases and their role thoroughly explained. In this Paper, our own Created Database i.e., (DETL) Database for Emotions in Telugu Language and the software Audacity for creating that database is discussed clearly.
Hybrid acoustic-deep features with auto encoders for speech emotion recognition K Raghu, M Sadanandam, B Hanumanthu Multimedia Tools and Applications 85 (2), 103 , 2026 2026
VOICE TO TEXT SUMMARIZATION USING NLP DKR N Sandeep Kumar, G Devasena, S Vishal Kumar International Journal on Computational Modelling Applications 1 (2), 1-9 , 2024 2024 Citations: 1
Design and Analysis of Embedded Sensors for IIoT: A Systematic Review MM Kogila Raghu AI-Driven IoT Systems for Industry 4.0 1, 21 , 2024 2024
Deep learning algorithms for speech emotion recognition with hybrid spectral features R Kogila, M Sadanandam, H Bhukya SN Computer Science 5 (1), 17 , 2023 2023 Citations: 5
Speech emotion recognition system performance analysis with optimized features using different classification algorithms K RAGHU, M SADANANDAM Journal of Theoretical and Applied Information Technology 101 (4) , 2023 2023 Citations: 1
Emotion Recognition from Speech Utterances with Hybrid Spectral Features Using Machine Learning Algorithms K Raghu, M Sadanandam IIETA-Traitement du Signal 39 (2), 603-609 , 2022 2022 Citations: 3
A Perspective Study on Speech Emotion Recognition: Databases, Features and Classification Models M Raghu, K., Sadanandam Traitement du Signal, 38, 1861-1873 , 2021 2021 Citations: 4
An Examination of Emotion Recognition using Machine Learning Algorithms on Different Speech Databases VKP Kogila Raghu, M Sadanandam IJITEE 9 (4S2), 37-39 , 2020 2020
Feature Extraction Techniques for Chronic Kidney Disease Identification. SB Satukumati, S Satla, R Kogila Ingénierie des Systèmes d'Information 24 (1) , 2019 2019 Citations: 8
Efficient Management of Server Platforms to meet Customer Requirements by using IMPI and FRU Techniques S.ShivaPrasad, Dr.M.Sadanandam, K.Raghu IJAER 13 , 2018 2018
MOST CITED SCHOLAR PUBLICATIONS
Feature Extraction Techniques for Chronic Kidney Disease Identification. SB Satukumati, S Satla, R Kogila Ingénierie des Systèmes d'Information 24 (1) , 2019 2019 Citations: 8
Deep learning algorithms for speech emotion recognition with hybrid spectral features R Kogila, M Sadanandam, H Bhukya SN Computer Science 5 (1), 17 , 2023 2023 Citations: 5
A Perspective Study on Speech Emotion Recognition: Databases, Features and Classification Models M Raghu, K., Sadanandam Traitement du Signal, 38, 1861-1873 , 2021 2021 Citations: 4
Emotion Recognition from Speech Utterances with Hybrid Spectral Features Using Machine Learning Algorithms K Raghu, M Sadanandam IIETA-Traitement du Signal 39 (2), 603-609 , 2022 2022 Citations: 3
VOICE TO TEXT SUMMARIZATION USING NLP DKR N Sandeep Kumar, G Devasena, S Vishal Kumar International Journal on Computational Modelling Applications 1 (2), 1-9 , 2024 2024 Citations: 1
Speech emotion recognition system performance analysis with optimized features using different classification algorithms K RAGHU, M SADANANDAM Journal of Theoretical and Applied Information Technology 101 (4) , 2023 2023 Citations: 1
Hybrid acoustic-deep features with auto encoders for speech emotion recognition K Raghu, M Sadanandam, B Hanumanthu Multimedia Tools and Applications 85 (2), 103 , 2026 2026
Design and Analysis of Embedded Sensors for IIoT: A Systematic Review MM Kogila Raghu AI-Driven IoT Systems for Industry 4.0 1, 21 , 2024 2024
An Examination of Emotion Recognition using Machine Learning Algorithms on Different Speech Databases VKP Kogila Raghu, M Sadanandam IJITEE 9 (4S2), 37-39 , 2020 2020
Efficient Management of Server Platforms to meet Customer Requirements by using IMPI and FRU Techniques S.ShivaPrasad, Dr.M.Sadanandam, K.Raghu IJAER 13 , 2018 2018
